ELLIS Health Workshop: Explainable Machine Learning & Biological Mechanisms
The ELLIS Health program (https://ellis.eu/programs/ellis-health) of the European Laboratory for Learning and Intelligent Systems organizes a series of online workshops in the wider area of machine learning and health. These workshop bring together external guest speakers with contributions from trainees from organising labs. The upcoming ELLIS Health Workshop on Explainable Machine Learning & Biological Mechanisms will focus on efforts to make machine learning interpretable and useful for understanding biology and advancing therapy.
Target audience:
- Machine learning researchers interested in applications biology and medicine
- Biologists and bioinformaticians who want to expand their knowledge and toolbox of machine learning methods
Organizers:
- Christoph Bock (CeMM Vienna & Medical University of Vienna) and
- Oliver Stegle (DKFZ Heidelberg & EMBL Heidelberg)
Date and format:
Zoom meeting, Wednesday 16 Dec 2020, 4pm to 6pm Central European Time.
https://embl-de.zoom.us/j/96821447288 (Passcode: 734443)
Scientific program:
- Invited talk (20 min talk + 5 min for questions): Jonas Peters (University of Copenhagen) - Title: Invariances, Causality and Stable Prediction
- Invited talk (20 min talk + 5 min for questions): Regina Barzilay (MIT) - Title: Deep learning in drug (and antibiotic) discovery
- Invited talk (20 min talk + 5 min for questions): Trey Ideker (UCSF) - Title: Interpretable deep learning to model the function of cells
- Short talk (10 min talk + 5 min for questions): Britta Velten (DKFZ Heidelberg) - Title: Probabilistic factor models for an interpretable integration of multi-modal omics data
- Short talk (10 min talk + 5 min for questions): Nikolaus Fortelny (CeMM Vienna & University of Salzburg) - Title: Biologically interpretable deep learning on single-cell data